sk.filter: split Knockoff filter for structural sparsity problem

In SplitKnockoff: Split Knockoffs for Structural Sparsity

split Knockoff filter for structural sparsity problem

Description

split Knockoff filter for structural sparsity problem

Usage

sk.filter(X, D, y, option)

Arguments

X	the design matrix
D	the response vector
y	the linear transformation
option	options for creating the Split Knockoff statistics. option$q: the desired FDR control target. option$beta: choices on beta(lambda), can be: 'path', beta(lambda) is taken from a regularization path; 'cv_beta', beta(lambda) is taken as the cross validation optimal estimator hat beta; or 'cv_all', beta(lambda) as well as nu are taken from the cross validation optimal estimators hat beta and hat nu.The default setting is 'cv_all'. option$lambda_cv: a set of lambda appointed for cross validation in estimating hat beta, default 10.^seq(0, -8, by = -0.4). option$nu_cv: a set of nu appointed for cross validation in estimating hat beta and hat nu, default 10.^seq(0, 2, by = 0.4). option$nu: a set of nu used in option.beta = 'path' or 'cv_beta' for Split Knockoffs, default 10.^seq(0, 2, by = 0.2). option$lambda: a set of lambda appointed for Split LASSO path calculation, default 10.^seq(0, -6, by = -0.01). option$normalize: whether to normalize the data, default true. option$W: the W statistics used for Split Knockoffs, can be 's', 'st', 'bc', 'bct', default 'st'.

Value

various intermedia statistics

SplitKnockoff documentation built on Oct. 14, 2024, 5:09 p.m.